Data Generation and Test Data Management Within the One Tool
Synthetic yet Privacy-Preserving Data Generation
Replace Your Real Data with Anonymized
TDspora is a privacy-preserving solution that replaces real data with semantically similar, yet anonymous data. The algorithm uses state-of-the-art differential privacy techniques to ensure the privacy of sensitive information while still providing meaningful insights. Differential privacy is widely used by government agencies and large enterprises, including FAANG companies, to safely release datasets to the public.
Choose Size of Data Set
The choice of the size of the dataset is decided based on balancing privacy and utility. Smaller datasets prioritize privacy, larger datasets prioritize utility with proper privacy measures. You can choose it by considering privacy budget, sensitivity of data and project goals.
Preserve Secured Perimeter Premises
TDspora users do not have access to production data and it remains within a secure perimeter, ensuring that sensitive information is protected.
Privacy-Preserving Data Generation
How does Your Team Benefit From the Generated Data?
Run an automated test suite against data that is as close to production as possible while preserving their ability to inspect the system in case of errors
Data Migration and Data Integration Teams
Get data with glitches and inconsistencies, including broken table relationships to test data validation rules and error handling routines
Distribute data that mirrors production to individual developers to ensure that new features are developed with the existing data in mind
Performance Testing Team
Produce a vast amount of data that repeats several behavioral patterns, but with slight variations
Get instant social portraits and behavioral patterns of product users by addressing the statistical properties of the dataset without the risk of exposing any user’s personal information
Who use scarce datasets can improve performance of classification and regression models by generation of more frequent case representation and using adjustable noise levels
Fault-Tolerant Data Replication and Advanced Sub-setting
Produce Representative Samples
Training of data generation models requires representative samples extracted from the highly repetitive production data. TDspora provides an advanced sub-setting algorithm that walks through relationships between tables and extracts dependent business entities.
Design Data Set
You can tailor the representative sample and make the replica as simple as a random sample or as complex as a subset of tables with filters and relationships:
Choose columns, tables, and relationships to send to the target database; Create tables and relationships in the target database; Filter tables in the sub-set; Manually define relationships.
Ensure Data Delivery
Automatic restarts ensure fault-tolerance in data delivery by preventing failure of the data copy process.
Plain Integrations with Popular Databases
Supported Authentication and Encryption Technologies:
On-demand Database Integrations
Implement a Database integration in less than 4 weeks due to flexible architecture and technologies like Apache Spark, JDBC, and jOOQ implemented in the tool.